


"""simple docstring"""
UpperCAmelCase				=						"""
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

UpperCAmelCase				=						[{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCAmelCase				=						{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   List[str]					=		logging.get_logger(__name__)

lowerCAmelCase:   Union[str, Any]					=		{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """lilt"""







    def __init__( self					:    str		,     __snake_case					:    Dict=3_05_22		,     __snake_case					:    int=7_68		,     __snake_case					:    Union[str, Any]=12		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Dict=30_72		,     __snake_case					:    Tuple="gelu"		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    List[str]=5_12		,     __snake_case					:    List[str]=2		,     __snake_case					:    str=0.02		,     __snake_case					:    Dict=1e-1_2		,     __snake_case					:    int=0		,     __snake_case					:    Any="absolute"		,     __snake_case					:    Tuple=None		,     __snake_case					:    Tuple=4		,     __snake_case					:    List[str]=10_24		,     **__snake_case					:    Union[str, Any]		,     ):
      super().__init__(pad_token_id=__snake_case		,     **__snake_case      )

      a		:   str	  =	vocab_size
      a		:   Optional[Any]	  =	hidden_size
      a		:   Union[str, Any]	  =	num_hidden_layers
      a		:   Tuple	  =	num_attention_heads
      a		:   Optional[Any]	  =	hidden_act
      a		:   Union[str, Any]	  =	intermediate_size
      a		:   Optional[Any]	  =	hidden_dropout_prob
      a		:   Tuple	  =	attention_probs_dropout_prob
      a		:   str	  =	max_position_embeddings
      a		:   Any	  =	type_vocab_size
      a		:   Union[str, Any]	  =	initializer_range
      a		:   int	  =	layer_norm_eps
      a		:   Any	  =	position_embedding_type
      a		:   Any	  =	classifier_dropout
      a		:   str	  =	channel_shrink_ratio
      a		:   Optional[Any]	  =	max_ad_position_embeddings